VoC Analyses Part 2: Regressions

Participant info

age_group	N	meanAge	sdAge	nFemale
Children	17	11.32	0.8799	8
Adolescents	29	15.37	1.518	14
Adults	46	22.11	2.36	25

Agency task: Machine selection

Model: Optimal machine choices across trials by condition and age

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: stage_2_acc ~ condition * zTrialOfCond * (zAge) + (condition * 
## Model:     zTrialOfCond | subID)
## Data: banditTask.filtered
## Df full model: 18
##                        Effect df     Chisq p.value
## 1                   condition  1 20.86 ***   <.001
## 2                zTrialOfCond  1 60.59 ***   <.001
## 3                        zAge  1      1.46    .227
## 4      condition:zTrialOfCond  1      0.08    .781
## 5              condition:zAge  1      0.21    .650
## 6           zTrialOfCond:zAge  1      1.46    .226
## 7 condition:zTrialOfCond:zAge  1      0.09    .770
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Predictor	Log-Odds	SE	p
intercept	2.38	0.17	<0.001
condition	-0.42	0.08	<0.001
trial	0.71	0.08	<0.001
age	0.21	0.17	0.225
condition x trial	-0.02	0.07	0.787
condition x age	-0.03	0.07	0.647
trial x age	0.10	0.08	0.223
condition x trial x age	0.02	0.06	0.763

Plot: Proportion optimal machine selections across age groups and trials

Agency task: Agency decisions

Model: Agency decisions by VoC

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: agency ~ zVoC * zTrialOfCond * zAge + (zVoC * zTrialOfCond | 
## Model:     subID)
## Data: banditTask
## Df full model: 18
##                   Effect df      Chisq p.value
## 1                   zVoC  1 144.39 ***   <.001
## 2           zTrialOfCond  1     3.56 +    .059
## 3                   zAge  1       0.01    .906
## 4      zVoC:zTrialOfCond  1  50.21 ***   <.001
## 5              zVoC:zAge  1     4.17 *    .041
## 6      zTrialOfCond:zAge  1       2.53    .111
## 7 zVoC:zTrialOfCond:zAge  1    9.59 **    .002
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Predictor	Log-Odds	SE	p
intercept	0.74	0.14	<0.001
VoC	1.42	0.07	<0.001
trial	0.10	0.05	0.059
age	-0.02	0.14	0.906
VoC x trial	0.33	0.04	<0.001
VoC x age	0.15	0.07	0.038
trial x age	-0.09	0.05	0.108
VoC x trial x age	0.12	0.04	0.001

Model: Agency decisions when VoC = 0

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: agency ~ zAge + (1 | subID)
## Data: banditTask.vocZeroTrials
## Df full model: 3
##   Effect df Chisq p.value
## 1   zAge  1  0.00    .961
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: agency ~ zAge + (1 | subID)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##   4304.7   4323.7  -2149.4   4298.7     4137 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2769 -0.6929  0.3759  0.6286  2.1169 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  subID  (Intercept) 1.24     1.113   
## Number of obs: 4140, groups:  subID, 92
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.246212   0.124404  10.017   <2e-16 ***
## zAge        -0.006025   0.123891  -0.049    0.961    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr)
## zAge -0.002

Plot: Sensitivity to the value of choice

Plot: Sensitivity to value of control with continuous age

Summary stats: Sensitivity to value of control

Agency decision reaction times

Model: Agency RTs by VoC

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: agencyLogRT ~ zAge * zAbsVoC * zTrialOfCond + (zAbsVoC * zTrialOfCond | 
## Model:     subID)
## Data: banditTask.RT.data
##                      Effect        df         F p.value
## 1                      zAge  1, 89.98    4.14 *    .045
## 2                   zAbsVoC  1, 90.30 16.96 ***   <.001
## 3              zTrialOfCond  1, 89.36 83.83 ***   <.001
## 4              zAge:zAbsVoC  1, 90.86      1.16    .285
## 5         zAge:zTrialOfCond  1, 89.48      0.48    .490
## 6      zAbsVoC:zTrialOfCond 1, 449.77      1.96    .162
## 7 zAge:zAbsVoC:zTrialOfCond 1, 455.28      0.01    .931
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Predictor	Estimates	SE	p
intercept	0.15	0.04	<0.001
age	-0.08	0.04	0.042
VoC magnitude	-0.02	0.01	<0.001
trial	-0.13	0.01	<0.001
age x VoC magnitude	-0.01	0.01	0.282
age x trial	0.01	0.01	0.489
VoC magnitude x trial	-0.00	0.00	0.161
age x VoC magnitude x trial	0.00	0.00	0.931

Plot: Agency RTs by VoC

Choice preference task

Choice preference task: summary stats

Model: Choice preference task accuracy

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: correct ~ zDiff * zAge + (zDiff | subID)
## Data: rewardSense.filtered
## Df full model: 7
##       Effect df     Chisq p.value
## 1      zDiff  1 79.85 ***   <.001
## 2       zAge  1      0.78    .376
## 3 zDiff:zAge  1      1.36    .244
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Explicit reward knowledge task

Explicit reward knowledge task: summary stats

Model: Explicit reward knowledge by age and true probabilities

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: error ~ zTrueProb * zAge + (1 | subID)
## Data: explicitKnow.filtered
##           Effect        df         F p.value
## 1      zTrueProb 1, 456.86 21.67 ***   <.001
## 2           zAge  1, 89.88      0.04    .845
## 3 zTrueProb:zAge 1, 456.86      0.36    .548
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Plot: Explicit reward knowledge